Crowd　counting　has　drawn　increasing　attention　across　various　fields.　However,　existing　crowd　counting　tasks　primarily　focus　on　estimating　the　overall　population,　ignoring　the　behavioral　and　semantic　information　of　different　social　groups　within　the　crowd.　In　this　paper,　we　aim　to　address　a　newly　proposed　research　problem,　namely　fine-grained　crowd　counting,　which　involves　identifying　different　categories　of　individuals　and　accurately　counting　them　in　static　images.　In　order　to　fully　leverage　the　categorical　information　in　static　crowd　images,　we　propose　a　two-tier　salient　feature　propagation　module　designed　to　sequentially　extract　semantic　information　from　both　the　crowd　and　its　surrounding　environment.　Additionally,　we　introduce　a　category　difference　loss　to　refine　the　feature　representation　by　highlighting　the　differences　between　various　crowd　categories.　Moreover,　our　proposed　framework　can　adapt　to　a　novel　problem　setup　called　few-example　fine-grained　crowd　counting.　This　setup,　unlike　the　original　fine-grained　crowd　counting,　requires　only　a　few　exemplar　point　annotations　instead　of　dense　annotations　from　predefined　categories,　making　it　applicable　in　a　wider　range　of　scenarios.　The　baseline　model　for　this　task　can　be　established　by　substituting　the　loss　function　in　our　proposed　model　with　a　novel　hybrid　loss　function　that　integrates　point-oriented　cross-entropy　loss　and　category　contrastive　loss.　Through　comprehensive　experiments,　we　present　results　in　both　the　formulation　and　application　of　fine-grained　crowd　counting.